Most state-of-the-art SLAM algorithms are framed as factor graph optimization problems where nodes represent robot poses or 3D landmarks in the environment, and edges (factors) are probabilistic constraints on these variables derived from sensor measurements. We use GTSAM [1]} as the underlying optimization framework, a library optimized for solving sparse factor graph representations. We refer the reader to [2]} for a detailed treatment of factor graphs.
